Directional Photofluidization Lithography for Nanoarchitectures with Controlled Shapes and Sizes
Bibliographic record
Abstract
Highly ordered metallic nanostructures have attracted an increasing interest in nanoscale electronics, photonics, and spectroscopic imaging. However, methods typically used for fabricating metallic nanostructures, such as direct writing and template-based nanolithography, have low throughput and are, moreover, limited to specific fabricated shapes such as holes, lines, and prisms, respectively. Herein, we demonstrate directional photofluidization lithography (DPL) as a new method to address the aforementioned problems of current nanolithography. The key idea of DPL is the use of photoreconfigurable polymer arrays to be molded in metallic nanostructures instead of conventional colloids or cross-linked polymer arrays. The photoreconfiguration of polymers by directional photofluidization allows unprecedented control over the sizes and shapes of metallic nanostructures. Besides the capability for precise control of structural features, DPL ensures scalable, parallel, and cost-effective processing, highly compatible with high-throughput fabrication. Therefore, DPL can expand not only the potential for specific metallic nanostructure applications but also large-scale innovative nanolithography.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".